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headroom

Agents & models

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Updated 10-15 23:48

About this skill

Headroom

Context compression for AI agents — reduce the tokens sent to an LLM by 60–95% while preserving meaning, by compressing tool outputs, logs, search results, RAG chunks, files, and conversation history before they reach the model. It auto-detects content type (JSON / code / text) and supports CCR (Compress-Cache-Retrieve): originals are cached locally and fetched on demand.

Upstream: headroom by chopratejas (Apache-2.0); published on PyPI as headroom-ai.

When to Use

  • A tool/command returns a large dump (verbose logs, big JSON, long file listings, search results, large files) that would bloat the context window.
  • The user asks to compress context, shrink a payload, or save tokens.
  • Compress before the large content is committed to context.

Setup

pip install "headroom-ai[mcp]"      # or [all] for every compressor / extra

Register the MCP server (exposes headroom_compress / headroom_retrieve / headroom_stats):

headroom mcp install        # auto-configures detected coding agents
# or run the server directly: headroom mcp serve

For full automatic compression of all traffic, run the proxy and point the client at it. This routes every API request through a local proxy, so enable it deliberately:

headroom proxy --port 8787
ANTHROPIC_BASE_URL=http://127.0.0.1:8787 claude

Tools (MCP, shown as mcp__headroom__*)

  • headroom_compress — compress a blob of context/content
  • headroom_retrieve — fetch the original uncompressed content (CCR)
  • headroom_stats — compression statistics / token savings

Use headroom_compress on a large output, keep the compressed summary in context, and call headroom_retrieve only when the full original is actually needed.

CLI extras: headroom perf (savings), headroom output-savings, headroom learn --verbosity, headroom update. Env toggles: HEADROOM_OUTPUT_SHAPER=1, HEADROOM_UPDATE_CHECK=off, HF_HUB_OFFLINE=1.

Installation notes & known issues

Windows source build fails — install inside WSL2 instead. headroom-ai includes a Rust extension (crates/headroom-py, built via maturin/cargo). On Windows there is no prebuilt wheel for recent Python, so pip/uv fall back to compiling from source — which fails when a GNU link (from Git / coreutils) shadows the MSVC link.exe linker (link: extra operand → could not compile target-lexicon → maturin failed). Rather than untangle the Windows Rust toolchain, install on the Linux side where wheels build cleanly:

# inside WSL2 Ubuntu (no sudo needed):
curl -LsSf https://astral.sh/uv/install.sh | sh      # if uv isn't present
uv tool install "headroom-ai[mcp]"                   # -> ~/.local/bin/headroom

Then register the MCP server from your Windows client by wrapping into WSL:

claude mcp add-json headroom \
  '{"command":"wsl.exe","args":["-d","Ubuntu","-e","bash","-lc","export PATH=$HOME/.local/bin:$PATH; exec headroom mcp serve"]}' \
  --scope user

On Linux/macOS, pip install "headroom-ai[mcp]" works directly — skip the WSL wrapper and register headroom mcp serve straight.

Credits

Upstream project: headroom by chopratejas (Apache-2.0); distributed on PyPI as headroom-ai. This skill documents and wires up the upstream tool for the Agent Skills format; the source is not vendored — install it from PyPI / the upstream repo.

Skill by Rinu (l3ad3r1) in collaboration with Claude (Anthropic).